Script to reproduce years based on a model trained with random points¶

Importing¶

In [ ]:
import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import TransformedTargetRegressor

from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor

from sklearn.metrics import root_mean_squared_error as rmse

from tqdm import tqdm

import dill
import random

import salishsea_tools.viz_tools as sa_vi

Datasets Preparation¶

In [ ]:
def datasets_preparation(dataset, dataset2):
    
    drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
        np.ravel(dataset['Temperature_(15m-100m)']), 
        np.ravel(dataset['Salinity_(0m-15m)']),
        np.ravel(dataset['Salinity_(15m-100m)']),
        np.ravel(dataset2['Summation_of_solar_radiation']),
        np.ravel(dataset2['Mean_wind_speed'])
        ])
    indx = np.where(~np.isnan(drivers).any(axis=0))
    drivers = drivers[:,indx[0]]

    diat = np.ravel(dataset['Diatom'])
    diat = diat[indx[0]]

    return(drivers, diat, indx)

Regressor¶

In [ ]:
def regressor (inputs, targets):
    
    inputs = inputs.transpose()
    
    # Regressor
    X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)

    model = DecisionTreeRegressor()
    model = make_pipeline(StandardScaler(), model)
    regr = BaggingRegressor(model, n_estimators=12, n_jobs=4).fit(X_train, y_train)

    return (regr)

Regressor 2¶

In [ ]:
def regressor2 (inputs, targets, variable_name):

    inputs2 = inputs.transpose()

    outputs_test = regr.predict(inputs2)
   
    m = scatter_plot(targets, outputs_test, variable_name) 
    r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
    rms = rmse(targets, outputs_test)

    return (r, rms, m)

Regressor 3¶

In [ ]:
def regressor3 (inputs, targets):
  
    inputs2 = inputs.transpose()

    outputs_test = regr.predict(inputs2)
   
    # compute slope m and intercept b
    m, b = np.polyfit(targets, outputs_test, deg=1)
    
    r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
    rms = rmse(targets, outputs_test)

    return (r, rms, m)

Regressor 4¶

In [ ]:
def regressor4 (inputs, targets, variable_name):

    inputs2 = inputs.transpose()
    
    outputs = regr.predict(inputs2)

    # Post processing
    indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
    indx2[indx[0]] = outputs
    model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))

    m = scatter_plot(targets, outputs, variable_name + str(dates[i].date())) 

    # Preparation of the dataarray 
    model = xr.DataArray(model,
        coords = {'y': diat_i.y, 'x': diat_i.x},
        dims = ['y','x'],
        attrs=dict( long_name = variable_name + "Concentration",
        units="mmol m-2"),)
                        
    plotting3(targets, model, diat_i, variable_name)

Printing¶

In [ ]:
def printing (targets, outputs, m):

    print ('The amount of data points is', outputs.size)
    print ('The slope of the best fitting line is ', np.round(m,3))
    print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
    print (' The mean square error is:', rmse(targets,outputs))

Scatter Plot¶

In [ ]:
def scatter_plot(targets, outputs, variable_name):

    # compute slope m and intercept b
    m, b = np.polyfit(targets, outputs, deg=1)

    printing(targets, outputs, m)

    fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')

    ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)

    lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
        np.max([ax[0].get_xlim(), ax[0].get_ylim()])]

    # plot fitted y = m*x + b
    ax[0].axline(xy1=(0, b), slope=m, color='r')

    ax[0].set_xlabel('targets')
    ax[0].set_ylabel('outputs')
    ax[0].set_xlim(lims)
    ax[0].set_ylim(lims)
    ax[0].set_aspect('equal')

    ax[0].plot(lims, lims,linestyle = '--',color = 'k')

    h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet', 
        range=[lims,lims], cmin=0.1, norm='log')
    
    ax[1].plot(lims, lims,linestyle = '--',color = 'k')

    # plot fitted y = m*x + b
    ax[1].axline(xy1=(0, b), slope=m, color='r')

    ax[1].set_xlabel('targets')
    ax[1].set_ylabel('outputs')
    ax[1].set_aspect('equal')

    fig.colorbar(h[3],ax=ax[1], location='bottom')

    fig.suptitle(variable_name)

    plt.show()

    return (m)

Plotting¶

In [ ]:
def plotting(variable, name):

    plt.plot(years,variable, marker = '.', linestyle = '')
    plt.xlabel('Years')
    plt.ylabel(name)
    plt.show()

Plotting 2¶

In [ ]:
def plotting2(variable,title):
    
    fig, ax = plt.subplots()

    scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)

    ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
    fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
    
    fig.show()

Plotting 3¶

In [ ]:
def plotting3(targets, model, variable, variable_name):

    fig, ax = plt.subplots(2,2, figsize = (10,15))

    cmap = plt.get_cmap('cubehelix')
    cmap.set_bad('gray')

    variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration  [mmol m-2]'})
    model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration  [mmol m-2]'})
    ((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration  [percentage]'})

    plt.subplots_adjust(left=0.1,
        bottom=0.1, 
        right=0.95, 
        top=0.95, 
        wspace=0.35, 
        hspace=0.35)

    sa_vi.set_aspect(ax[0,0])
    sa_vi.set_aspect(ax[0,1])
    sa_vi.set_aspect(ax[1,0])


    ax[0,0].title.set_text(variable_name + ' (targets)')
    ax[0,1].title.set_text(variable_name + ' (outputs)')
    ax[1,0].title.set_text('targets - outputs')
    ax[1,1].axis('off')

    fig.suptitle(str(dates[i].date()))

    plt.show()
    

Training (Random Points)¶

In [ ]:
ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds2 = xr.open_dataset('/data/ibougoudis/MOAD/files/external_inputs.nc')

# ds = ds.isel(time_counter = (np.arange(0, len(ds.time_counter),2)), 
#     y=(np.arange(ds.y[0], ds.y[-1], 5)), 
#     x=(np.arange(ds.x[0], ds.x[-1], 5)))

# ds2 = ds2.isel(time_counter = (np.arange(0, len(ds2.time_counter),2)), 
#     y=(np.arange(ds2.y[0], ds2.y[-1], 5)), 
#     x=(np.arange(ds2.x[0], ds2.x[-1], 5)))

dates = pd.DatetimeIndex(ds['time_counter'].values)

drivers, diat, _ = datasets_preparation(ds, ds2)

regr = regressor(drivers, diat)

Other Years (Anually)¶

In [ ]:
years = range (2007,2024)

r_all = []
rms_all = []
slope_all = []

for year in tqdm(range (2007,2024)):
    
    dataset = ds.sel(time_counter=str(year))
    dataset2 = ds2.sel(time_counter=str(year))
    
    drivers, diat, _ = datasets_preparation(dataset, dataset2)

    r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
    
    r_all.append(r)
    rms_all.append(rms)
    slope_all.append(m)
    
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
  0%|          | 0/17 [00:00<?, ?it/s]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.497
The correlation coefficient is: 0.977
 The mean square error is: 0.03490124707966972
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  6%|▌         | 1/17 [00:43<11:40, 43.76s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404
The slope of the best fitting line is  0.494
The correlation coefficient is: 0.965
 The mean square error is: 0.038075280649577825
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 12%|█▏        | 2/17 [01:32<11:42, 46.84s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.492
The correlation coefficient is: 0.98
 The mean square error is: 0.03971866669458346
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 18%|█▊        | 3/17 [02:13<10:13, 43.85s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.499
The correlation coefficient is: 0.977
 The mean square error is: 0.03129580954597224
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 24%|██▎       | 4/17 [02:53<09:10, 42.33s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.5
The correlation coefficient is: 0.98
 The mean square error is: 0.031358808976161265
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 29%|██▉       | 5/17 [03:34<08:22, 41.92s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404
The slope of the best fitting line is  0.498
The correlation coefficient is: 0.978
 The mean square error is: 0.03337135030295379
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 35%|███▌      | 6/17 [04:14<07:34, 41.30s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.499
The correlation coefficient is: 0.976
 The mean square error is: 0.03942226122754738
No description has been provided for this image
 41%|████      | 7/17 [04:53<06:44, 40.49s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.499
The correlation coefficient is: 0.971
 The mean square error is: 0.03470721681990617
No description has been provided for this image
 47%|████▋     | 8/17 [05:32<06:00, 40.09s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.498
The correlation coefficient is: 0.983
 The mean square error is: 0.028094215093718407
No description has been provided for this image
 53%|█████▎    | 9/17 [06:12<05:20, 40.01s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404
The slope of the best fitting line is  0.498
The correlation coefficient is: 0.987
 The mean square error is: 0.027744103586922458
No description has been provided for this image
 59%|█████▉    | 10/17 [06:49<04:33, 39.12s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.497
The correlation coefficient is: 0.975
 The mean square error is: 0.029849395828214702
No description has been provided for this image
 65%|██████▍   | 11/17 [07:29<03:56, 39.34s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.493
The correlation coefficient is: 0.971
 The mean square error is: 0.03751021885163527
No description has been provided for this image
 71%|███████   | 12/17 [08:06<03:13, 38.78s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.494
The correlation coefficient is: 0.972
 The mean square error is: 0.04162589518162528
No description has been provided for this image
 76%|███████▋  | 13/17 [08:44<02:34, 38.54s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3532404
The slope of the best fitting line is  0.49
The correlation coefficient is: 0.975
 The mean square error is: 0.045365657283306424
No description has been provided for this image
 82%|████████▏ | 14/17 [09:23<01:55, 38.52s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.494
The correlation coefficient is: 0.981
 The mean square error is: 0.03440497182532782
No description has been provided for this image
 88%|████████▊ | 15/17 [10:03<01:18, 39.00s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.499
The correlation coefficient is: 0.977
 The mean square error is: 0.03157936497975302
No description has been provided for this image
 94%|█████████▍| 16/17 [10:44<00:39, 39.55s/it]/tmp/ipykernel_25358/1980467486.py:4: RankWarning: Polyfit may be poorly conditioned
  m, b = np.polyfit(targets, outputs, deg=1)
The amount of data points is 3485925
The slope of the best fitting line is  0.498
The correlation coefficient is: 0.972
 The mean square error is: 0.03898835566089332
No description has been provided for this image
100%|██████████| 17/17 [11:25<00:00, 40.30s/it]
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Other Years (Daily)¶

In [ ]:
r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])

for i in tqdm(range (0, len(ds.time_counter))):
    
    dataset = ds.isel(time_counter=i)
    dataset2 = ds2.isel(time_counter=i)

    drivers, diat, _ = datasets_preparation(dataset, dataset2)

    r, rms, m = regressor3(drivers, diat)

    r_all2 = np.append(r_all2,r)
    rms_all2 = np.append(rms_all2,rms)
    slope_all2 = np.append(slope_all2,m)

plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
100%|██████████| 1279/1279 [11:40:36<00:00, 32.87s/it]  
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Daily Maps¶

In [ ]:
maps = random.sample(range(0,len(ds.time_counter)),10)

for i in tqdm(maps):

    dataset = ds.isel(time_counter=i)
    dataset2 = ds2.isel(time_counter=i)
    drivers, diat, indx = datasets_preparation(dataset, dataset2)

    diat_i = dataset['Diatom']

    regressor4(drivers, diat, 'Diatom ')
  0%|          | 0/10 [00:00<?, ?it/s]
The amount of data points is 46479
The slope of the best fitting line is  0.861
The correlation coefficient is: 0.945
 The mean square error is: 0.042138232345826206
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 10%|█         | 1/10 [00:35<05:19, 35.50s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.869
The correlation coefficient is: 0.958
 The mean square error is: 0.05927678601072566
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 20%|██        | 2/10 [01:09<04:38, 34.78s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.938
The correlation coefficient is: 0.966
 The mean square error is: 0.04157926302484591
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 30%|███       | 3/10 [01:41<03:52, 33.27s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.893
The correlation coefficient is: 0.948
 The mean square error is: 0.021576014985365415
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 40%|████      | 4/10 [02:12<03:14, 32.38s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.972
The correlation coefficient is: 0.968
 The mean square error is: 0.013524567365046338
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 50%|█████     | 5/10 [02:43<02:39, 31.84s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.902
The correlation coefficient is: 0.933
 The mean square error is: 0.0203533569691006
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 60%|██████    | 6/10 [03:15<02:08, 32.08s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.874
The correlation coefficient is: 0.879
 The mean square error is: 0.02558484523511505
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 70%|███████   | 7/10 [03:45<01:34, 31.43s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.93
The correlation coefficient is: 0.959
 The mean square error is: 0.034261297471123386
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 80%|████████  | 8/10 [04:17<01:02, 31.38s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.938
The correlation coefficient is: 0.967
 The mean square error is: 0.01607637032995785
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 90%|█████████ | 9/10 [04:47<00:31, 31.07s/it]
The amount of data points is 46479
The slope of the best fitting line is  0.907
The correlation coefficient is: 0.958
 The mean square error is: 0.018836434030312137
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100%|██████████| 10/10 [05:18<00:00, 31.84s/it]
100%|██████████| 10/10 [05:18<00:00, 31.84s/it]
In [ ]: